Vertaile menetelmiä

	Robust simple linear regression	OLS-regressio (Ordinary Least Squares)
Tieteenala	Tilastotiede	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1964-1987	2019
Kehittäjä	Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)	Wooldridge (textbook treatment); classical least squares
Tyyppi	Robust linear regression	Linear regression
Alkuperäislähde	Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rinnakkaisnimet	robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	6	5
Tiivistelmä	Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).